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Normalizing Flows on Tori and Spheres
Danilo J. Rezende · George Papamakarios · Sebastien Racaniere · Michael Albergo · Gurtej Kanwar · Phiala Shanahan · Kyle Cranmer

Tue Jul 14 12:00 PM -- 12:45 PM & Wed Jul 15 01:00 AM -- 01:45 AM (PDT) @ Virtual #None

Normalizing flows are a powerful tool for building expressive distributions in high dimensions. So far, most of the literature has concentrated on learning flows on Euclidean spaces. Some problems however, such as those involving angles, are defined on spaces with more complex geometries, such as tori or spheres. In this paper, we propose and compare expressive and numerically stable flows on such spaces. Our flows are built recursively on the dimension of the space, starting from flows on circles, closed intervals or spheres.

Author Information

Danilo J. Rezende (DeepMind)
Danilo J. Rezende

Danilo is a Senior Staff Research Scientist at Google DeepMind, where he works on probabilistic machine reasoning and learning algorithms. He has a BA in Physics and MSc in Theoretical Physics from Ecole Polytechnique (Palaiseau – France) and from the Institute of Theoretical Physics (SP – Brazil) and a Ph.D. in Computational Neuroscience at Ecole Polytechnique Federale de Lausanne, EPFL (Lausanne – Switzerland). His research focuses on scalable inference methods, generative models of complex data (such as images and video), applied probability, causal reasoning and unsupervised learning for decision-making.

George Papamakarios (DeepMind)
Sebastien Racaniere (DeepMind)
Michael Albergo (New York University)
Gurtej Kanwar (Massachusetts Institute of Technology)
Phiala Shanahan (Massachusetts Institute of Technology)
Kyle Cranmer (New York University, CERN)

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